Mammography has been used in screening of breast cancer. The goal of screening mammography is the early detection of breast cancer through detection of masses and/or microcalcifications. Mammograms use doses of ionizing radiation usually at a lower-energy X-ray level (usually around 30 kVp).
The U.S. Preventive Services Task Force (USPSTF) recommended in 2009 screening of women aged between 50 and 74 with mammography every two years. The Canadian Task Force on Preventive Health Care and the European Cancer Observatory recommends mammography every 2-3 years between 50 and 69.
The radiation exposure associated with mammography is a potential risk of screening. The risk of exposure appears to be greater in younger women. The largest study of radiation risk from mammography concluded that for women 40 years of age or older, the risk of radiation-induced breast cancer was acceptable, particularly compared with the potential benefit of mammographic screening, with a benefit-to-risk ratio of 48.5 lives saved for each life lost due to radiation exposure 11. Organizations such as the National Cancer Institute and USPSTF take such risks into account when formulating screening guidelines [2].
However, a study by Yaffe and Mainprize [3] predicted that there would be 86 cancers induced and 11 deaths due to radiation-induced breast cancer among a cohort of 100,000 women each receiving a dose of 3.7 mGy to both breasts and who were screened annually from age 40 to 55 years and biennially thereafter to age 74 years.
The majority of health experts agree that the risk of breast cancer for asymptomatic women under 35 is not high enough to warrant the risk of radiation exposure. For this reason, and because the radiation sensitivity of the breast in women under 35 is possibly greater than in older women, most radiologists will not perform screening mammography in women under 40.
When a radiologist or a computer detects, interprets, analyzes, and diagnoses mammograms, there is a tradeoff between radiation dose levels and image quality. Higher radiation doses result in higher signal-to-noise ratio, while lower doses lead to increased image noise including quantum noise and electronic noise. The higher radiation would increase the risk of radiation-induced cancer. Therefore, it is important to reduce radiation exposures and doses as much as possible, or radiation exposures and doses should be kept as low as reasonably achievable.
Researchers have studied radiation dose reduction in mammography. S. Obenauer et al. [4] compared full-field digital mammography with screen-film mammography, and investigated a potential of dose reduction with an anthropomorphic breast phantom by changing anode-filter combinations in full-field digital mammography. R. L. Smathers et al. [5] evaluated the effects of anode-filter combinations on radiation dose reduction in mammography with 206 clinical cases. They showed that changing anode-filter combinations could reduce radiation dose by 35%. X. Liu et al. [6] investigated the effects of exposure equalization on the image quality and radiation dose reduction with an anthropomorphic breast phantom. They showed that the expose equalization technique could reduce radiation dose by 34%. M. Yakabe et al. [7] investigated the relationship between radiation dose and the detectability of simulated microcalcifications with an anthropomorphic breast phantom. The radiation dose was changed by changing tube-current-time-product, mAs. W. Huda et al. [8] investigated the effects of random noise and lesion size on the detection performance by radiologists with an anthropomorphic breast phantom. A. S. Chawla et al. [9] investigated the effect of radiation dose reduction on the detection of breast lesions with simulated noise by using mathematical observer model analysis. G. Gennaro et al. [10] evaluated the phantom use in radiation dose reduction in mammography. N. T. Ranger et al. [11] investigated optimization of tube voltage, kVp and anode-filter combinations in mammography. E. Samei et al. [12] assessed the relationship between radiation dose and observer accuracy in the detection of simulated lesions in mammography. K. C. Young et al. [13] assessed the automatic beam quality selection function of a vendor's mammography system.
On the other hand, computer-aided diagnostic (CAD) systems are being tested to decrease the number of cases of cancer that are missed in mammograms. In one test, a computer identified 71% of the cases of cancer that had been missed by physicians. However, the computer also flagged twice as many non-cancerous masses than the physicians did. In a second study of a larger set of mammograms, a computer recommended six biopsies that physicians did not. All six turned out to be cancers that would have been missed [14].
In the field of CAD, K. Suzuki et al. developed a pixel-based machine-learning technique based on an artificial neural network (ANN), called massive-training ANNs (MTANN), for distinguishing a specific opacity (pattern) from other opacities (patterns) in 2D CT images [15]. An MTANN was developed by extension of neural filters [16] and a neural edge enhancer [17] to accommodate various pattern-recognition and classification tasks [15]. The 2D MTANN was applied to reduction of false positives (FPs) in computerized detection of lung nodules on 2D CT slices in a slice-by-slice way [15, 18, 19] and in chest radiographs [20], the separation of ribs from soft tissue in chest radiographs [21-23], and the distinction between benign and malignant lung nodules on 2D CT slices [24]. For processing of three-dimensional (3D) volume data, a 3D MTANN was developed by extending the structure of the 2D MTANN, and it was applied to 3D CT colonography data [25-29].